This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.
Knit to html for best performance.
Calculation is based on BONUS GOHERR project and its http://en.opasnet.org/w/Goherr_assessment.
What needs to be done for PFAS assessment?
objects.latest("Op_en2261", code_name="RR2") # RR on page [[Health impact assessment]]
## Loading objects:
## RR
RRorig <- RR@formula
RR@formula <- function(...) {
out <- RRorig()
out <- out * Ovariable(output=data.frame(Age=c(
"0 - 4", "5 - 9", "10 - 14", "15 - 19", "20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54",
"55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85+"),
Result=1),
marginal=c(TRUE,FALSE)
)
return(out)
}
# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]
dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)
CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)
cat("Laskennassa käytetty data.\n")
## Laskennassa käytetty data.
dat
cat("Tarkastellut päätökset.\n")
## Tarkastellut päätökset.
dec
cat("Aggregoidut marginaalit.\n")
## Aggregoidut marginaalit.
CTable
dummy <- Ovariable("dummy",data=data.frame(Age="dummy", Fish="dummy", Compound="dummy", Area="dummy", Result=1)) # Keep these columns marginals
fish_proportion <- Ovariable( # How population subgroups eat fish differently
"fish_proportion",
data = data.frame(prepare(dat,"fish proportion",c("Type","Exposure_agent","Response","Unit"))),
unit="proportion of the mean")
total_amount <- Ovariable(
"total_amount",
data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")),
unit="M kg/a")
amount <- Ovariable(
"amount",
dependencies = data.frame(Name=c("total_amount", "fish_proportion")),
formula = function(...) {
amount <- total_amount
# Filleted weight, i.e. no loss.
amount <- amount * 1000 / 5.52 /365.25
# M kg/a per 5.52M population --> g/d per average person.
amount <- amount * fish_proportion
# fish_proportion tells the relative amount in each subgroup
# Match KKE-classification in amount with Fineli classification
tmp <- Ovariable(
output = data.frame(
Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
Result = 1
),
marginal = c(TRUE, TRUE, FALSE)
)
amount <- amount * tmp
return(amount)
},
unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included
population <- Ovariable(
"population",
data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
unit = "#"
)
incidence <- Ovariable(
"incidence",
data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""
case_burden <- Ovariable(
"case_burden",
data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
unit="DALY/case")
ERFchoice <- Ovariable(
"ERFchoice",
data =
prepare(dat, "ERFchoice", c("Unit", "Type"))
)
InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
"InpBoD",
data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"
conc_vit <- Ovariable(
"conc_vit",
ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
subset = "Fineli data for common fish species",
unit="ALA mg/g, DHA mg/g, Fish g/g, Omega3 mg/g, Vitamin D µg/g f.w. after adjustment"
)
df = conc_vit@data
df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df
######## Concentration of PFAS
# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
# POP mean sd min Q0.025 median Q0.975 max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935
# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
# POP mean sd min Q0.025 median Q0.975 max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994
conc_eukalat <- EvalOutput(Ovariable(
"conc_eukalat",
data = data.frame(
Area = c("Suomi","Helsinki"),
Compound="PFOS",
Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
unit="ng/g fresh weight"
))
conc_pfas_raw <- Ovariable(
"conc_pfas_raw",
ddata="Op_fi5932",subset="PFAS concentrations",
unit="ng/g f.w.")
conc_pfas <- Ovariable(
"conc_pfas",
dependencies = data.frame(Name="conc_pfas_raw"),
formula = function(...) {
out <- conc_pfas_raw
out$Source <- NULL
out$Area <- "Porvoo"
out <- expand_index(out, list(Fish=list(Perch=c(
"Average fish", "Pike","Rainbow trout","Roach", "Salmon", "Vendace", "Whitefish"))))
out@marginal <- c(TRUE, TRUE, FALSE, TRUE, TRUE)
return(out)
},
unit="ng/g fresh weight"
)
#sum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
#tmp <- conc_pfas / sum_pfas
#summary(tmp, marginals="Compound")
#
## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.
conc <- Ovariable(
"conc",
dependencies = data.frame(Name=c("conc_vit", "conc_pfas")),
formula = function(...){
conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"
conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean)
conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
conc_pfas <- oapply(conc_pfas, cols="", FUN=sum)
out <- OpasnetUtils::combine(conc_vit, conc_pfas)
return(out)
}
)
###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#
mc2dparam<- list(
N2 = 10, # Number of iterations in the new Iter
strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
run2d = FALSE, # Should the mc2d function be used or not?
info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
newmarginals = c("Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
fun = mean # Function for aggregating the first Iter dimension.
)
if(FALSE) {
## Exposure with background exposure but without mother's exposure to child
expo_dir <- Ovariable(
"expo_dir",
dependencies=data.frame(Name=c("amount","conc","expo_bg")),
formula = function(...) {
out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
out$Exposure_agent <- "Fish"
out <- combine(conc, out, name="conc")
out <- oapply(amount * out, cols="Fish", FUN=sum)
out <- Ovariable(output = data.frame(
Exposcen = c("BAU", "No exposure"),
Result = c(1, 0)
), marginal=c(TRUE,FALSE)) * out + expo_bg
out$Exposure <- as.factor(
ifelse(
out$Exposure_agent %in% c("DHA", "MeHg"),
"To child",
"To eater"
)
)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)
## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
"addexposure",
ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
subset = "Background exposure",
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
"expo_bg",
dependencies = data.frame(Name="addexposure","info"),
formula = function(...) {
out <- addexposure
# Empty values ("") in indices must be replaced by NA so that Ops works correctly.
levels(out$Gender)[levels(out$Gender) == ""] <- NA
levels(out$Country)[levels(out$Country) == ""] <- NA
levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
temp1$Exposure_agent <- "TEQ"
temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
temp2$Exposure_agent <- "Omega3"
out <- OpasnetUtils::combine(out, temp1, temp2)
out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Stores non-marginal columns for further use.
info <- Ovariable(
"info",
dependencies = data.frame(Name = c("jsp")),
formula = function(...) {
out <- jsp
out$Group <- factor(
paste(out$Gender, out$Ages),
levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
)
out$Country <- factor(out$Country, ordered=FALSE)
out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
out$Result <- 1
return(out)
}
)
} # END IF
############################### Code from Goherr assessment ends
expo_dir <- Ovariable(
"expo_dir",
dependencies = data.frame(Name=c("conc", "amount")),
formula = function(...) {
conc$Fish[conc$Fish %in% c("Perch","Pike-perch","Eel","")] <- "Average fish"
conc <- oapply(conc, cols="", FUN=mean)
out <- conc * amount
# colnames(out@output)[colnames(out@output)=="Nutrient"] <- "Exposure_agent"
return(out)
},
unit="ng/d"
)
exposure <- Ovariable(
"exposure",
dependencies = data.frame(
Name = c(
"expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
"expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
"mc2d" # 2D Monte Carlo function
),
Ident = c(
NA,
"Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
"Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
),
formula = function(...) {
out <- OpasnetUtils::combine(expo_dir, expo_indir)
out <- unkeep(out, "Source.1", sources=TRUE)
out <- mc2d(out)
out$Exposure[is.na(out$Exposure)] <- "Direct"
return(out)
},
unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)
exposure <- Ovariable(
"exposure",
dependencies=data.frame(Name=c("amount","conc")),
formula = function(...) {
out <- amount * conc
colnames(out@output)[colnames(out@output)=="Compound"] <- "Exposure_agent"
return(out)
}
)
objects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]
## Loading objects:
## BoDattr
tryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))
## Evaluating BoDattr ...
##
## - BoD fetched successfully!
##
## - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
##
## done(0.01 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## Loading required package: reshape2
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
##
## smiths
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
##
## done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
##
## done(0 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
##
## - done(0.3 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
## done!
## - Evaluating PAF ...
##
## - - dose fetched successfully!
##
## - - ERF fetched successfully!
##
## - - frexposed fetched successfully!
##
## - - P_illness fetched successfully!
##
## - - sumExposcen fetched successfully!
##
## - - mc2d fetched successfully!
## - - Evaluating dose ...
##
## - - - BW fetched successfully!
## - - - Evaluating exposure ...
## - - - - Evaluating amount ...
## - - - - - Evaluating total_amount ...
##
## done(0.01 secs)!
## - - - - - Checking total_amount marginals ... Kala, Scenario, total_amountSource recognized as marginal(s).
## - - - - - Evaluating fish_proportion ...
##
## done(0 secs)!
## - - - - - Checking fish_proportion marginals ... Gender, fish_proportionSource recognized as marginal(s).
##
## ---- done(0.14 secs)!
## - - - - Checking amount marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource recognized as marginal(s).
## - - - - Evaluating conc ...
## - - - - - Evaluating conc_vit ...
##
## done(0.01 secs)!
## - - - - - Checking conc_vit marginals ... Kala, Fish, Nutrient, conc_vitSource recognized as marginal(s).
## - - - - - Processing conc_vit decisions ... done!
## - - - - - Evaluating conc_pfas ...
## - - - - - - Evaluating conc_pfas_raw ...
##
## done(0.03 secs)!
## - - - - - - Checking conc_pfas_raw marginals ... Fish, Compound, conc_pfas_rawSource recognized as marginal(s).
##
## ----- done(0.09 secs)!
## - - - - - Checking conc_pfas marginals ... Fish, Compound, conc_pfas_rawSource, Area, conc_pfasSource recognized as marginal(s).
##
## ---- done(0.22 secs)!
## - - - - Checking conc marginals ... Fish, Compound, conc_pfas_rawSource, concSource recognized as marginal(s).
##
## --- done(0.46 secs)!
## - - - Checking exposure marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource, Exposure_agent, conc_pfas_rawSource, concSource, exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying exposure, found NAs in indices: conc_pfas_rawSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
## done!
## - - - Evaluating BW ...
##
## done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
##
## -- done(25.3 secs)!
## - - Checking dose marginals ... Scenario, total_amountSource, Gender, fish_proportionSource, amountSource, Exposure_agent, conc_pfas_rawSource, concSource, Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
##
## - - - ERF_env fetched successfully!
##
## - - - ERF_omega3 fetched successfully!
##
## - - - ERF_mehg fetched successfully!
##
## - - - ERF_diox fetched successfully!
##
## - - - ERF_vit fetched successfully!
##
## - - - ERF_micr fetched successfully!
##
## - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
##
## done(0.02 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
##
## done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
##
## done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
##
## done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
##
## done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
##
## done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
##
## done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
##
## done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
##
## -- done(2.92 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
##
## -- done(0.16 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, total_amountSource, Gender, fish_proportionSource, amountSource, conc_pfas_rawSource, doseSource, Age, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
##
## done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
##
## done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
##
## - done(5.85 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, total_amountSource, Gender, fish_proportionSource, amountSource, conc_pfas_rawSource, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
## done!
##
## done(6.66 mins)!
## Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, Scenario, total_amountSource, fish_proportionSource, amountSource, conc_pfas_rawSource, PAFSource, BoDattrSource recognized as marginal(s).
oprint(summary(amount,"mean"))
## Kala Scenario Gender Fish mean
## 1 Kaupallinen BAU Female Average fish 1.2697279
## 2 Muu tuonti BAU Female Average fish 11.1101191
## 3 Vapaa-ajan BAU Female Average fish 3.9282207
## 4 Kaupallinen BAU Male Average fish 1.9045919
## 5 Muu tuonti BAU Male Average fish 16.6651787
## 6 Vapaa-ajan BAU Male Average fish 5.8923310
## 7 Silakka BAU Female Herring 0.6348640
## 8 Silakka BAU Male Herring 0.9522959
## 9 Kirjolohi BAU Female Rainbow trout 2.6584928
## 10 Tuontikirjolohi BAU Female Rainbow trout 1.9045919
## 11 Kirjolohi BAU Male Rainbow trout 3.9877392
## 12 Tuontikirjolohi BAU Male Rainbow trout 2.8568878
## 13 Tuontilohi BAU Female Salmon 9.3642433
## 14 Tuontilohi BAU Male Salmon 14.0463649
## 15 Kasvatettu BAU Female Whitefish 0.2380740
## 16 Kasvatettu BAU Male Whitefish 0.3571110
oprint(summary(BoD,marginals=c("Age","Response"),"mean"))
## Age Response mean
## 1 0 - 4 Cancer morbidity 309.5200
## 2 10 - 14 Cancer morbidity 347.0750
## 3 15 - 19 Cancer morbidity 414.9750
## 4 20 - 24 Cancer morbidity 569.5250
## 5 25 - 29 Cancer morbidity 795.4550
## 6 30 - 34 Cancer morbidity 1147.9850
## 7 35 - 39 Cancer morbidity 1679.1250
## 8 40 - 44 Cancer morbidity 2452.5650
## 9 45 - 49 Cancer morbidity 3849.3800
## 10 5 - 9 Cancer morbidity 345.5200
## 11 50 - 54 Cancer morbidity 6987.9000
## 12 55 - 59 Cancer morbidity 11237.3300
## 13 60 - 64 Cancer morbidity 16331.9100
## 14 65 - 69 Cancer morbidity 21899.6500
## 15 70 - 74 Cancer morbidity 25551.8600
## 16 75 - 79 Cancer morbidity 18015.2300
## 17 80 - 84 Cancer morbidity 14650.9150
## 18 85 - 89 Cancer morbidity 8413.0550
## 19 90 - 94 Cancer morbidity 3537.1750
## 20 Undefined Dioxin recommendation tolerable daily intake 305.4067
## 21 Undefined Dioxin recommendation tolerable daily intake 2018 888.4560
## 22 0 - 4 Immunosuppression 295.9850
## 23 5 - 9 Immunosuppression 261.5350
## 24 0 - 4 Loss in child's IQ points 16778.3774
## 25 Undefined PFAS TWI 2776.4249
## 26 0 - 4 Sperm concentration 4478.6700
## 27 Undefined Vitamin D recommendation 610.8135
## 28 0 - 4 Yes or no dental defect 343.9619
## 29 0 - 4 All-cause mortality 4867.4650
## 30 10 - 14 All-cause mortality 1085.5450
## 31 15 - 19 All-cause mortality 2965.9450
## 32 20 - 24 All-cause mortality 5274.8450
## 33 25 - 29 All-cause mortality 6263.5400
## 34 30 - 34 All-cause mortality 6710.3100
## 35 35 - 39 All-cause mortality 8275.6450
## 36 40 - 44 All-cause mortality 10232.0150
## 37 45 - 49 All-cause mortality 13691.2800
## 38 5 - 9 All-cause mortality 931.1250
## 39 50 - 54 All-cause mortality 21321.8650
## 40 55 - 59 All-cause mortality 29861.9100
## 41 60 - 64 All-cause mortality 39200.8750
## 42 65 - 69 All-cause mortality 49295.5050
## 43 70 - 74 All-cause mortality 59799.7000
## 44 75 - 79 All-cause mortality 49428.5150
## 45 80 - 84 All-cause mortality 51968.9150
## 46 85 - 89 All-cause mortality 42765.3450
## 47 90 - 94 All-cause mortality 26756.6400
## 48 0 - 4 Depression 0.4150
## 49 10 - 14 Depression 555.8850
## 50 15 - 19 Depression 1320.8250
## 51 20 - 24 Depression 1841.0150
## 52 25 - 29 Depression 1801.4650
## 53 30 - 34 Depression 1590.0200
## 54 35 - 39 Depression 1721.7250
## 55 40 - 44 Depression 1700.2800
## 56 45 - 49 Depression 1534.1700
## 57 5 - 9 Depression 76.4850
## 58 50 - 54 Depression 1718.9550
## 59 55 - 59 Depression 1767.1650
## 60 60 - 64 Depression 1676.7400
## 61 65 - 69 Depression 1561.5750
## 62 70 - 74 Depression 1426.2350
## 63 75 - 79 Depression 799.7000
## 64 80 - 84 Depression 581.2950
## 65 85 - 89 Depression 363.0750
## 66 90 - 94 Depression 186.1150
## 67 0 - 4 CHD2 mortality 53.6100
## 68 10 - 14 CHD2 mortality 75.3150
## 69 15 - 19 CHD2 mortality 145.3450
## 70 20 - 24 CHD2 mortality 254.1000
## 71 25 - 29 CHD2 mortality 437.6450
## 72 30 - 34 CHD2 mortality 636.4200
## 73 35 - 39 CHD2 mortality 1168.2600
## 74 40 - 44 CHD2 mortality 1977.5650
## 75 45 - 49 CHD2 mortality 3117.3100
## 76 5 - 9 CHD2 mortality 48.7800
## 77 50 - 54 CHD2 mortality 5476.2900
## 78 55 - 59 CHD2 mortality 8747.4650
## 79 60 - 64 CHD2 mortality 13077.9700
## 80 65 - 69 CHD2 mortality 18100.7500
## 81 70 - 74 CHD2 mortality 24549.2850
## 82 75 - 79 CHD2 mortality 23413.4850
## 83 80 - 84 CHD2 mortality 27899.3350
## 84 85 - 89 CHD2 mortality 25240.0350
## 85 90 - 94 CHD2 mortality 16630.6150
## 86 15 - 19 Breast cancer 2.0900
## 87 20 - 24 Breast cancer 7.7850
## 88 25 - 29 Breast cancer 36.8200
## 89 30 - 34 Breast cancer 145.1600
## 90 35 - 39 Breast cancer 263.6000
## 91 40 - 44 Breast cancer 433.3500
## 92 45 - 49 Breast cancer 704.9550
## 93 50 - 54 Breast cancer 1045.3250
## 94 55 - 59 Breast cancer 1341.8150
## 95 60 - 64 Breast cancer 1513.2150
## 96 65 - 69 Breast cancer 1649.3300
## 97 70 - 74 Breast cancer 1678.0350
## 98 75 - 79 Breast cancer 1232.0100
## 99 80 - 84 Breast cancer 950.8800
## 100 85 - 89 Breast cancer 578.1900
## 101 90 - 94 Breast cancer 296.1800
oprint(summary(BoDattr,marginals=c("Age","Response"),"mean"))
## Age Response mean
## 1 0 - 4 Immunosuppression 6.818641e+00
## 2 5 - 9 Immunosuppression 7.294478e+00
## 3 0 - 4 Loss in child's IQ points -3.292014e+03
## 4 Undefined PFAS TWI 1.370935e+03
## 5 Undefined Vitamin D recommendation 6.108135e+02
## 6 0 - 4 All-cause mortality -3.940215e+02
## 7 10 - 14 All-cause mortality -8.729181e+01
## 8 15 - 19 All-cause mortality -2.510075e+02
## 9 20 - 24 All-cause mortality -4.582433e+02
## 10 25 - 29 All-cause mortality -5.472993e+02
## 11 30 - 34 All-cause mortality -5.805901e+02
## 12 35 - 39 All-cause mortality -7.164739e+02
## 13 40 - 44 All-cause mortality -8.793680e+02
## 14 45 - 49 All-cause mortality -1.166310e+03
## 15 5 - 9 All-cause mortality -7.424681e+01
## 16 50 - 54 All-cause mortality -1.808052e+03
## 17 55 - 59 All-cause mortality -2.535812e+03
## 18 60 - 64 All-cause mortality -3.319267e+03
## 19 65 - 69 All-cause mortality -4.145066e+03
## 20 70 - 74 All-cause mortality -4.980650e+03
## 21 75 - 79 All-cause mortality -4.044850e+03
## 22 80 - 84 All-cause mortality -4.134937e+03
## 23 0 - 4 Depression -7.647127e-02
## 24 10 - 14 Depression -9.937062e+01
## 25 15 - 19 Depression -2.347323e+02
## 26 20 - 24 Depression -3.276647e+02
## 27 25 - 29 Depression -3.226701e+02
## 28 30 - 34 Depression -2.865779e+02
## 29 35 - 39 Depression -3.110681e+02
## 30 40 - 44 Depression -3.073361e+02
## 31 45 - 49 Depression -2.766914e+02
## 32 5 - 9 Depression -1.387565e+01
## 33 50 - 54 Depression -3.090770e+02
## 34 55 - 59 Depression -3.166728e+02
## 35 60 - 64 Depression -2.997941e+02
## 36 65 - 69 Depression -2.791761e+02
## 37 70 - 74 Depression -2.551780e+02
## 38 75 - 79 Depression -1.425020e+02
## 39 80 - 84 Depression -1.024717e+02
## 40 0 - 4 CHD2 mortality -8.359787e+00
## 41 10 - 14 CHD2 mortality -1.170566e+01
## 42 15 - 19 CHD2 mortality -2.262663e+01
## 43 20 - 24 CHD2 mortality -3.966234e+01
## 44 25 - 29 CHD2 mortality -6.844631e+01
## 45 30 - 34 CHD2 mortality -9.964232e+01
## 46 35 - 39 CHD2 mortality -1.833859e+02
## 47 40 - 44 CHD2 mortality -3.106654e+02
## 48 45 - 49 CHD2 mortality -4.899542e+02
## 49 5 - 9 CHD2 mortality -7.579246e+00
## 50 50 - 54 CHD2 mortality -8.613391e+02
## 51 55 - 59 CHD2 mortality -1.375930e+03
## 52 60 - 64 CHD2 mortality -2.055932e+03
## 53 65 - 69 CHD2 mortality -2.841726e+03
## 54 70 - 74 CHD2 mortality -3.847268e+03
## 55 75 - 79 CHD2 mortality -3.660036e+03
## 56 80 - 84 CHD2 mortality -4.346514e+03
## 57 15 - 19 Breast cancer -4.624153e-01
## 58 20 - 24 Breast cancer -1.587543e+00
## 59 25 - 29 Breast cancer -7.326368e+00
## 60 30 - 34 Breast cancer -2.873340e+01
## 61 35 - 39 Breast cancer -5.218217e+01
## 62 40 - 44 Breast cancer -8.577757e+01
## 63 45 - 49 Breast cancer -1.395958e+02
## 64 50 - 54 Breast cancer -2.070689e+02
## 65 55 - 59 Breast cancer -2.658472e+02
## 66 60 - 64 Breast cancer -2.998331e+02
## 67 65 - 69 Breast cancer -3.267095e+02
## 68 70 - 74 Breast cancer -3.327787e+02
## 69 75 - 79 Breast cancer -2.441402e+02
## 70 80 - 84 Breast cancer -1.884558e+02
oprint(summary(BoDattr,marginals=c("Exposure_agent","Response"),"mean"))
## Exposure_agent Response mean
## 1 PFAS Immunosuppression 7.056559
## 2 DHA Loss in child's IQ points -3292.013959
## 3 PFAS PFAS TWI 1370.934688
## 4 Vitamin D Vitamin D recommendation 610.813471
## 5 Fish All-cause mortality -1771.969747
## 6 Fish Depression -228.525606
## 7 Omega3 CHD2 mortality -1190.045495
## 8 Omega3 Breast cancer -155.749901
oprint(summary(BoDattr,marginals=c("Gender","Response"),"mean"))
## Gender Response mean
## 1 Female Immunosuppression 5.205862
## 2 Male Immunosuppression 8.907256
## 3 Female Loss in child's IQ points -2561.773339
## 4 Male Loss in child's IQ points -4022.254579
## 5 Female PFAS TWI 0.000000
## 6 Male PFAS TWI 2741.869376
## 7 Female Vitamin D recommendation 618.415679
## 8 Male Vitamin D recommendation 603.211263
## 9 Female All-cause mortality -1003.248948
## 10 Male All-cause mortality -2540.690546
## 11 Female Depression -235.343301
## 12 Male Depression -221.707910
## 13 Female CHD2 mortality -815.643510
## 14 Male CHD2 mortality -1564.447480
## 15 Female Breast cancer -308.799252
## 16 Male Breast cancer -2.700550
oprint(summary(case_burden,"mean"))
## Response mean
## 1 Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3 Loss in child's IQ points 0.110000000
## 4 PFAS TWI 0.001004988
## 5 Sperm concentration 2.500000000
## 6 Vitamin D recommendation 0.001004988
## 7 Yes or no dental defect 0.060000000
oprint(summary(conc,"mean"))
## Fish Compound mean
## 1 Average fish ALA 0.690000
## 2 Bream ALA 0.220000
## 3 Herring ALA 1.740000
## 4 Pike ALA 0.080000
## 5 Rainbow trout ALA 4.810000
## 6 Roach ALA 0.100000
## 7 Salmon ALA 7.960000
## 8 Vendace ALA 1.350000
## 9 Whitefish ALA 2.220000
## 10 Average fish DHA 2.540000
## 11 Bream DHA 2.730000
## 12 Herring DHA 5.860000
## 13 Pike DHA 0.300000
## 14 Rainbow trout DHA 7.570000
## 15 Roach DHA 2.870000
## 16 Salmon DHA 6.690000
## 17 Vendace DHA 3.000000
## 18 Whitefish DHA 3.940000
## 19 Average fish Fish 1.000000
## 20 Bream Fish 1.000000
## 21 Herring Fish 1.000000
## 22 Pike Fish 1.000000
## 23 Rainbow trout Fish 1.000000
## 24 Roach Fish 1.000000
## 25 Salmon Fish 1.000000
## 26 Vendace Fish 1.000000
## 27 Whitefish Fish 1.000000
## 28 Average fish Omega3 7.000000
## 29 Bream Omega3 6.000000
## 30 Herring Omega3 24.000000
## 31 Pike Omega3 0.500000
## 32 Rainbow trout Omega3 18.000000
## 33 Roach Omega3 5.000000
## 34 Salmon Omega3 23.000000
## 35 Vendace Omega3 10.000000
## 36 Whitefish Omega3 10.000000
## 37 Average fish PFAS 8.793125
## 38 Bream PFAS 4.950000
## 39 Eel PFAS 7.895000
## 40 Herring PFAS 1.655000
## 41 Perch PFAS 8.793125
## 42 Pike PFAS 8.793125
## 43 Pike-perch PFAS 2.595000
## 44 Rainbow trout PFAS 8.793125
## 45 Roach PFAS 8.793125
## 46 Salmon PFAS 8.793125
## 47 Vendace PFAS 8.793125
## 48 Whitefish PFAS 8.793125
## 49 Average fish Vitamin D 0.105000
## 50 Bream Vitamin D 0.140000
## 51 Herring Vitamin D 0.156000
## 52 Pike Vitamin D 0.021000
## 53 Rainbow trout Vitamin D 0.051000
## 54 Roach Vitamin D 0.100000
## 55 Salmon Vitamin D 0.067000
## 56 Vendace Vitamin D 0.094000
## 57 Whitefish Vitamin D 0.144000
oprint(summary(dose,"mean"))
## Scenario Gender Exposure_agent Scaling mean
## 1 BAU Female ALA BW 1.56247954
## 2 BAU Male ALA BW 2.34371931
## 3 BAU Female DHA BW 2.04671635
## 4 BAU Male DHA BW 3.07007453
## 5 BAU Female Fish BW 0.44440477
## 6 BAU Male Fish BW 0.66660715
## 7 BAU Female Omega3 BW 6.13267239
## 8 BAU Male Omega3 BW 9.19900859
## 9 BAU Female PFAS BW 3.84296754
## 10 BAU Male PFAS BW 5.76445130
## 11 BAU Female Vitamin D BW 0.03865415
## 12 BAU Male Vitamin D BW 0.05798122
## 13 BAU Female ALA Log10 2.03891238
## 14 BAU Male ALA Log10 2.21500364
## 15 BAU Female DHA Log10 2.15615570
## 16 BAU Male DHA Log10 2.33224696
## 17 BAU Female Fish Log10 1.49287675
## 18 BAU Male Fish Log10 1.66896801
## 19 BAU Female Omega3 Log10 2.63274781
## 20 BAU Male Omega3 Log10 2.80883906
## 21 BAU Female PFAS Log10 2.42976476
## 22 BAU Male PFAS Log10 2.60585601
## 23 BAU Female Vitamin D Log10 0.43229411
## 24 BAU Male Vitamin D Log10 0.60838537
## 25 BAU Female ALA None 109.37356784
## 26 BAU Male ALA None 164.06035175
## 27 BAU Female DHA None 143.27014453
## 28 BAU Male DHA None 214.90521680
## 29 BAU Female Fish None 31.10833358
## 30 BAU Male Fish None 46.66250037
## 31 BAU Female Omega3 None 429.28706762
## 32 BAU Male Omega3 None 643.93060143
## 33 BAU Female PFAS None 269.00772748
## 34 BAU Male PFAS None 403.51159123
## 35 BAU Female Vitamin D None 2.70579016
## 36 BAU Male Vitamin D None 4.05868524
oprint(summary(ERF,"mean"))
## Exposure_agent Response
## 1 Fish All-cause mortality
## 2 Omega3 Breast cancer
## 3 Fish Depression
## 4 DHA Loss in child's IQ points
## 5 TEQ Sperm concentration
## 6 TEQ Yes or no dental defect
## 7 Omega3 CHD2 mortality
## 8 Vitamin D Vitamin D recommendation
## 9 MeHg Loss in child's IQ points
## 10 PFAS Immunosuppression
## 11 PFAS PFAS TWI
## 12 TEQ Cancer morbidity
## 13 TEQ Dioxin recommendation tolerable daily intake
## 14 TEQ Dioxin recommendation tolerable daily intake 2018
## 15 Fish All-cause mortality
## 16 Omega3 Breast cancer
## 17 Fish Depression
## 18 DHA Loss in child's IQ points
## 19 TEQ Sperm concentration
## 20 TEQ Yes or no dental defect
## 21 Omega3 CHD2 mortality
## 22 Vitamin D Vitamin D recommendation
## 23 MeHg Loss in child's IQ points
## 24 PFAS Immunosuppression
## 25 PFAS PFAS TWI
## 26 TEQ Cancer morbidity
## 27 TEQ Dioxin recommendation tolerable daily intake
## 28 TEQ Dioxin recommendation tolerable daily intake 2018
## ER_function Scaling Observation mean
## 1 RR None ERF 0.997871700
## 2 RR None ERF 0.999487200
## 3 RR None ERF 0.994690400
## 4 ERS None ERF -0.001300000
## 5 ERS None ERF 0.000060000
## 6 ERS None ERF 0.001390971
## 7 Relative Hill None ERF -0.170000000
## 8 Step None ERF 100.000000000
## 9 ERS BW ERF 9.800000000
## 10 ERS BW ERF 0.022700000
## 11 TWI BW ERF 4.400000000
## 12 CSF BW ERF 0.000500000
## 13 TDI BW ERF 2.000000000
## 14 TDI BW ERF 0.288900000
## 15 RR None Threshold 0.000000000
## 16 RR None Threshold 0.000000000
## 17 RR None Threshold 0.000000000
## 18 ERS None Threshold 0.000000000
## 19 ERS None Threshold 0.000000000
## 20 ERS None Threshold 0.000000000
## 21 Relative Hill None Threshold 47.000000000
## 22 Step None Threshold 10.000000000
## 23 ERS BW Threshold 0.000000000
## 24 ERS BW Threshold 0.000000000
## 25 TWI BW Threshold 0.000000000
## 26 CSF BW Threshold 0.000000000
## 27 TDI BW Threshold 0.000000000
## 28 TDI BW Threshold 0.000000000
oprint(summary(expo_dir,"mean"))
## Warning in oapply(conc, cols = "", FUN = mean): While oapplying conc, found NAs
## in indices: conc_pfas_rawSource. They were automatically filled using fillna,
## which may result in a multiplied population. Please check your ovariable before
## using oapply.
## Fish Compound Kala Scenario Gender mean
## 1 Whitefish ALA Kasvatettu BAU Female 0.52852424
## 2 Whitefish DHA Kasvatettu BAU Female 0.93801149
## 3 Whitefish Fish Kasvatettu BAU Female 0.23807398
## 4 Whitefish Omega3 Kasvatettu BAU Female 2.38073981
## 5 Whitefish PFAS Kasvatettu BAU Female 2.09341428
## 6 Whitefish Vitamin D Kasvatettu BAU Female 0.03428265
## 7 Average fish ALA Kaupallinen BAU Female 0.87611225
## 8 Average fish DHA Kaupallinen BAU Female 3.22510887
## 9 Average fish Fish Kaupallinen BAU Female 1.26972790
## 10 Average fish Omega3 Kaupallinen BAU Female 8.88809531
## 11 Average fish PFAS Kaupallinen BAU Female 8.91229950
## 12 Average fish Vitamin D Kaupallinen BAU Female 0.13332143
## 13 Rainbow trout ALA Kirjolohi BAU Female 12.78735034
## 14 Rainbow trout DHA Kirjolohi BAU Female 20.12479045
## 15 Rainbow trout Fish Kirjolohi BAU Female 2.65849279
## 16 Rainbow trout Omega3 Kirjolohi BAU Female 47.85287028
## 17 Rainbow trout PFAS Kirjolohi BAU Female 23.37645944
## 18 Rainbow trout Vitamin D Kirjolohi BAU Female 0.13558313
## 19 Average fish ALA Muu tuonti BAU Female 7.66598220
## 20 Average fish DHA Muu tuonti BAU Female 28.21970261
## 21 Average fish Fish Muu tuonti BAU Female 11.11011914
## 22 Average fish Omega3 Muu tuonti BAU Female 77.77083395
## 23 Average fish PFAS Muu tuonti BAU Female 77.98262060
## 24 Average fish Vitamin D Muu tuonti BAU Female 1.16656251
## 25 Herring ALA Silakka BAU Female 1.10466327
## 26 Herring DHA Silakka BAU Female 3.72030275
## 27 Herring Fish Silakka BAU Female 0.63486395
## 28 Herring Omega3 Silakka BAU Female 15.23673482
## 29 Herring PFAS Silakka BAU Female 1.05069984
## 30 Herring Vitamin D Silakka BAU Female 0.09903878
## 31 Rainbow trout ALA Tuontikirjolohi BAU Female 9.16108681
## 32 Rainbow trout DHA Tuontikirjolohi BAU Female 14.41776032
## 33 Rainbow trout Fish Tuontikirjolohi BAU Female 1.90459185
## 34 Rainbow trout Omega3 Tuontikirjolohi BAU Female 34.28265333
## 35 Rainbow trout PFAS Tuontikirjolohi BAU Female 16.74731423
## 36 Rainbow trout Vitamin D Tuontikirjolohi BAU Female 0.09713418
## 37 Salmon ALA Tuontilohi BAU Female 74.53937644
## 38 Salmon DHA Tuontilohi BAU Female 62.64678749
## 39 Salmon Fish Tuontilohi BAU Female 9.36424327
## 40 Salmon Omega3 Tuontilohi BAU Female 215.37759525
## 41 Salmon PFAS Tuontilohi BAU Female 82.34096162
## 42 Salmon Vitamin D Tuontilohi BAU Female 0.62740430
## 43 Average fish ALA Vapaa-ajan BAU Female 2.71047228
## 44 Average fish DHA Vapaa-ajan BAU Female 9.97768056
## 45 Average fish Fish Vapaa-ajan BAU Female 3.92822069
## 46 Average fish Omega3 Vapaa-ajan BAU Female 27.49754486
## 47 Average fish PFAS Vapaa-ajan BAU Female 27.57242657
## 48 Average fish Vitamin D Vapaa-ajan BAU Female 0.41246317
## 49 Whitefish ALA Kasvatettu BAU Male 0.79278636
## 50 Whitefish DHA Kasvatettu BAU Male 1.40701723
## 51 Whitefish Fish Kasvatettu BAU Male 0.35711097
## 52 Whitefish Omega3 Kasvatettu BAU Male 3.57110972
## 53 Whitefish PFAS Kasvatettu BAU Male 3.14012142
## 54 Whitefish Vitamin D Kasvatettu BAU Male 0.05142398
## 55 Average fish ALA Kaupallinen BAU Male 1.31416838
## 56 Average fish DHA Kaupallinen BAU Male 4.83766330
## 57 Average fish Fish Kaupallinen BAU Male 1.90459185
## 58 Average fish Omega3 Kaupallinen BAU Male 13.33214296
## 59 Average fish PFAS Kaupallinen BAU Male 13.36844925
## 60 Average fish Vitamin D Kaupallinen BAU Male 0.19998214
## 61 Rainbow trout ALA Kirjolohi BAU Male 19.18102550
## 62 Rainbow trout DHA Kirjolohi BAU Male 30.18718567
## 63 Rainbow trout Fish Kirjolohi BAU Male 3.98773919
## 64 Rainbow trout Omega3 Kirjolohi BAU Male 71.77930542
## 65 Rainbow trout PFAS Kirjolohi BAU Male 35.06468916
## 66 Rainbow trout Vitamin D Kirjolohi BAU Male 0.20337470
## 67 Average fish ALA Muu tuonti BAU Male 11.49897331
## 68 Average fish DHA Muu tuonti BAU Male 42.32955391
## 69 Average fish Fish Muu tuonti BAU Male 16.66517870
## 70 Average fish Omega3 Muu tuonti BAU Male 116.65625093
## 71 Average fish PFAS Muu tuonti BAU Male 116.97393090
## 72 Average fish Vitamin D Muu tuonti BAU Male 1.74984376
## 73 Herring ALA Silakka BAU Male 1.65699491
## 74 Herring DHA Silakka BAU Male 5.58045413
## 75 Herring Fish Silakka BAU Male 0.95229593
## 76 Herring Omega3 Silakka BAU Male 22.85510222
## 77 Herring PFAS Silakka BAU Male 1.57604976
## 78 Herring Vitamin D Silakka BAU Male 0.14855816
## 79 Rainbow trout ALA Tuontikirjolohi BAU Male 13.74163021
## 80 Rainbow trout DHA Tuontikirjolohi BAU Male 21.62664048
## 81 Rainbow trout Fish Tuontikirjolohi BAU Male 2.85688778
## 82 Rainbow trout Omega3 Tuontikirjolohi BAU Male 51.42398000
## 83 Rainbow trout PFAS Tuontikirjolohi BAU Male 25.12097134
## 84 Rainbow trout Vitamin D Tuontikirjolohi BAU Male 0.14570128
## 85 Salmon ALA Tuontilohi BAU Male 111.80906467
## 86 Salmon DHA Tuontilohi BAU Male 93.97018123
## 87 Salmon Fish Tuontilohi BAU Male 14.04636491
## 88 Salmon Omega3 Tuontilohi BAU Male 323.06639288
## 89 Salmon PFAS Tuontilohi BAU Male 123.51144243
## 90 Salmon Vitamin D Tuontilohi BAU Male 0.94110645
## 91 Average fish ALA Vapaa-ajan BAU Male 4.06570842
## 92 Average fish DHA Vapaa-ajan BAU Male 14.96652085
## 93 Average fish Fish Vapaa-ajan BAU Male 5.89233104
## 94 Average fish Omega3 Vapaa-ajan BAU Male 41.24631729
## 95 Average fish PFAS Vapaa-ajan BAU Male 41.35863985
## 96 Average fish Vitamin D Vapaa-ajan BAU Male 0.61869476
#oprint(summary(exposure,"mean"))
#oprint(summary(fish_proportion,"mean"))
oprint(summary(incidence,"mean"))
## Response Age Adjust mean
## 1 Immunosuppression 0 - 4 BAU 4.8100
## 2 Loss in child's IQ points 0 - 4 BAU 1.1920
## 3 Sperm concentration 0 - 4 BAU 0.0140
## 4 Yes or no dental defect 0 - 4 BAU 0.0448
## 5 Immunosuppression 5 - 9 BAU 3.9500
## 6 Dioxin recommendation tolerable daily intake Undefined BAU 0.1100
## 7 Dioxin recommendation tolerable daily intake 2018 Undefined BAU 0.3200
## 8 PFAS TWI Undefined BAU 1.0000
## 9 Vitamin D recommendation Undefined BAU 0.2200
oprint(summary(PAF,"mean"))
## Exposure_agent Response Scenario Gender Age Adjust
## 1 DHA Loss in child's IQ points BAU Female 0 - 4 BAU
## 2 PFAS Immunosuppression BAU Female 0 - 4 BAU
## 3 Fish All-cause mortality BAU Female 0 - 4 BAU
## 4 Omega3 Breast cancer BAU Female 0 - 4 BAU
## 5 Fish Depression BAU Female 0 - 4 BAU
## 6 Omega3 CHD2 mortality BAU Female 0 - 4 BAU
## 7 DHA Loss in child's IQ points BAU Male 0 - 4 BAU
## 8 PFAS Immunosuppression BAU Male 0 - 4 BAU
## 9 Fish All-cause mortality BAU Male 0 - 4 BAU
## 10 Omega3 Breast cancer BAU Male 0 - 4 BAU
## 11 Fish Depression BAU Male 0 - 4 BAU
## 12 Omega3 CHD2 mortality BAU Male 0 - 4 BAU
## 13 Fish All-cause mortality BAU Female 10 - 14 BAU
## 14 Omega3 Breast cancer BAU Female 10 - 14 BAU
## 15 Fish Depression BAU Female 10 - 14 BAU
## 16 Omega3 CHD2 mortality BAU Female 10 - 14 BAU
## 17 Fish All-cause mortality BAU Male 10 - 14 BAU
## 18 Omega3 Breast cancer BAU Male 10 - 14 BAU
## 19 Fish Depression BAU Male 10 - 14 BAU
## 20 Omega3 CHD2 mortality BAU Male 10 - 14 BAU
## 21 Fish All-cause mortality BAU Female 15 - 19 BAU
## 22 Omega3 Breast cancer BAU Female 15 - 19 BAU
## 23 Fish Depression BAU Female 15 - 19 BAU
## 24 Omega3 CHD2 mortality BAU Female 15 - 19 BAU
## 25 Fish All-cause mortality BAU Male 15 - 19 BAU
## 26 Omega3 Breast cancer BAU Male 15 - 19 BAU
## 27 Fish Depression BAU Male 15 - 19 BAU
## 28 Omega3 CHD2 mortality BAU Male 15 - 19 BAU
## 29 Fish All-cause mortality BAU Female 20 - 24 BAU
## 30 Omega3 Breast cancer BAU Female 20 - 24 BAU
## 31 Fish Depression BAU Female 20 - 24 BAU
## 32 Omega3 CHD2 mortality BAU Female 20 - 24 BAU
## 33 Fish All-cause mortality BAU Male 20 - 24 BAU
## 34 Omega3 Breast cancer BAU Male 20 - 24 BAU
## 35 Fish Depression BAU Male 20 - 24 BAU
## 36 Omega3 CHD2 mortality BAU Male 20 - 24 BAU
## 37 Fish All-cause mortality BAU Female 25 - 29 BAU
## 38 Omega3 Breast cancer BAU Female 25 - 29 BAU
## 39 Fish Depression BAU Female 25 - 29 BAU
## 40 Omega3 CHD2 mortality BAU Female 25 - 29 BAU
## 41 Fish All-cause mortality BAU Male 25 - 29 BAU
## 42 Omega3 Breast cancer BAU Male 25 - 29 BAU
## 43 Fish Depression BAU Male 25 - 29 BAU
## 44 Omega3 CHD2 mortality BAU Male 25 - 29 BAU
## 45 Fish All-cause mortality BAU Female 30 - 34 BAU
## 46 Omega3 Breast cancer BAU Female 30 - 34 BAU
## 47 Fish Depression BAU Female 30 - 34 BAU
## 48 Omega3 CHD2 mortality BAU Female 30 - 34 BAU
## 49 Fish All-cause mortality BAU Male 30 - 34 BAU
## 50 Omega3 Breast cancer BAU Male 30 - 34 BAU
## 51 Fish Depression BAU Male 30 - 34 BAU
## 52 Omega3 CHD2 mortality BAU Male 30 - 34 BAU
## 53 Fish All-cause mortality BAU Female 35 - 39 BAU
## 54 Omega3 Breast cancer BAU Female 35 - 39 BAU
## 55 Fish Depression BAU Female 35 - 39 BAU
## 56 Omega3 CHD2 mortality BAU Female 35 - 39 BAU
## 57 Fish All-cause mortality BAU Male 35 - 39 BAU
## 58 Omega3 Breast cancer BAU Male 35 - 39 BAU
## 59 Fish Depression BAU Male 35 - 39 BAU
## 60 Omega3 CHD2 mortality BAU Male 35 - 39 BAU
## 61 Fish All-cause mortality BAU Female 40 - 44 BAU
## 62 Omega3 Breast cancer BAU Female 40 - 44 BAU
## 63 Fish Depression BAU Female 40 - 44 BAU
## 64 Omega3 CHD2 mortality BAU Female 40 - 44 BAU
## 65 Fish All-cause mortality BAU Male 40 - 44 BAU
## 66 Omega3 Breast cancer BAU Male 40 - 44 BAU
## 67 Fish Depression BAU Male 40 - 44 BAU
## 68 Omega3 CHD2 mortality BAU Male 40 - 44 BAU
## 69 Fish All-cause mortality BAU Female 45 - 49 BAU
## 70 Omega3 Breast cancer BAU Female 45 - 49 BAU
## 71 Fish Depression BAU Female 45 - 49 BAU
## 72 Omega3 CHD2 mortality BAU Female 45 - 49 BAU
## 73 Fish All-cause mortality BAU Male 45 - 49 BAU
## 74 Omega3 Breast cancer BAU Male 45 - 49 BAU
## 75 Fish Depression BAU Male 45 - 49 BAU
## 76 Omega3 CHD2 mortality BAU Male 45 - 49 BAU
## 77 PFAS Immunosuppression BAU Female 5 - 9 BAU
## 78 Fish All-cause mortality BAU Female 5 - 9 BAU
## 79 Omega3 Breast cancer BAU Female 5 - 9 BAU
## 80 Fish Depression BAU Female 5 - 9 BAU
## 81 Omega3 CHD2 mortality BAU Female 5 - 9 BAU
## 82 PFAS Immunosuppression BAU Male 5 - 9 BAU
## 83 Fish All-cause mortality BAU Male 5 - 9 BAU
## 84 Omega3 Breast cancer BAU Male 5 - 9 BAU
## 85 Fish Depression BAU Male 5 - 9 BAU
## 86 Omega3 CHD2 mortality BAU Male 5 - 9 BAU
## 87 Fish All-cause mortality BAU Female 50 - 54 BAU
## 88 Omega3 Breast cancer BAU Female 50 - 54 BAU
## 89 Fish Depression BAU Female 50 - 54 BAU
## 90 Omega3 CHD2 mortality BAU Female 50 - 54 BAU
## 91 Fish All-cause mortality BAU Male 50 - 54 BAU
## 92 Omega3 Breast cancer BAU Male 50 - 54 BAU
## 93 Fish Depression BAU Male 50 - 54 BAU
## 94 Omega3 CHD2 mortality BAU Male 50 - 54 BAU
## 95 Fish All-cause mortality BAU Female 55 - 59 BAU
## 96 Omega3 Breast cancer BAU Female 55 - 59 BAU
## 97 Fish Depression BAU Female 55 - 59 BAU
## 98 Omega3 CHD2 mortality BAU Female 55 - 59 BAU
## 99 Fish All-cause mortality BAU Male 55 - 59 BAU
## 100 Omega3 Breast cancer BAU Male 55 - 59 BAU
## 101 Fish Depression BAU Male 55 - 59 BAU
## 102 Omega3 CHD2 mortality BAU Male 55 - 59 BAU
## 103 Fish All-cause mortality BAU Female 60 - 64 BAU
## 104 Omega3 Breast cancer BAU Female 60 - 64 BAU
## 105 Fish Depression BAU Female 60 - 64 BAU
## 106 Omega3 CHD2 mortality BAU Female 60 - 64 BAU
## 107 Fish All-cause mortality BAU Male 60 - 64 BAU
## 108 Omega3 Breast cancer BAU Male 60 - 64 BAU
## 109 Fish Depression BAU Male 60 - 64 BAU
## 110 Omega3 CHD2 mortality BAU Male 60 - 64 BAU
## 111 Fish All-cause mortality BAU Female 65 - 69 BAU
## 112 Omega3 Breast cancer BAU Female 65 - 69 BAU
## 113 Fish Depression BAU Female 65 - 69 BAU
## 114 Omega3 CHD2 mortality BAU Female 65 - 69 BAU
## 115 Fish All-cause mortality BAU Male 65 - 69 BAU
## 116 Omega3 Breast cancer BAU Male 65 - 69 BAU
## 117 Fish Depression BAU Male 65 - 69 BAU
## 118 Omega3 CHD2 mortality BAU Male 65 - 69 BAU
## 119 Fish All-cause mortality BAU Female 70 - 74 BAU
## 120 Omega3 Breast cancer BAU Female 70 - 74 BAU
## 121 Fish Depression BAU Female 70 - 74 BAU
## 122 Omega3 CHD2 mortality BAU Female 70 - 74 BAU
## 123 Fish All-cause mortality BAU Male 70 - 74 BAU
## 124 Omega3 Breast cancer BAU Male 70 - 74 BAU
## 125 Fish Depression BAU Male 70 - 74 BAU
## 126 Omega3 CHD2 mortality BAU Male 70 - 74 BAU
## 127 Fish All-cause mortality BAU Female 75 - 79 BAU
## 128 Omega3 Breast cancer BAU Female 75 - 79 BAU
## 129 Fish Depression BAU Female 75 - 79 BAU
## 130 Omega3 CHD2 mortality BAU Female 75 - 79 BAU
## 131 Fish All-cause mortality BAU Male 75 - 79 BAU
## 132 Omega3 Breast cancer BAU Male 75 - 79 BAU
## 133 Fish Depression BAU Male 75 - 79 BAU
## 134 Omega3 CHD2 mortality BAU Male 75 - 79 BAU
## 135 Fish All-cause mortality BAU Female 80 - 84 BAU
## 136 Omega3 Breast cancer BAU Female 80 - 84 BAU
## 137 Fish Depression BAU Female 80 - 84 BAU
## 138 Omega3 CHD2 mortality BAU Female 80 - 84 BAU
## 139 Fish All-cause mortality BAU Male 80 - 84 BAU
## 140 Omega3 Breast cancer BAU Male 80 - 84 BAU
## 141 Fish Depression BAU Male 80 - 84 BAU
## 142 Omega3 CHD2 mortality BAU Male 80 - 84 BAU
## 143 Fish All-cause mortality BAU Female 85+ BAU
## 144 Omega3 Breast cancer BAU Female 85+ BAU
## 145 Fish Depression BAU Female 85+ BAU
## 146 Omega3 CHD2 mortality BAU Female 85+ BAU
## 147 Fish All-cause mortality BAU Male 85+ BAU
## 148 Omega3 Breast cancer BAU Male 85+ BAU
## 149 Fish Depression BAU Male 85+ BAU
## 150 Omega3 CHD2 mortality BAU Male 85+ BAU
## 151 Vitamin D Vitamin D recommendation BAU Female Undefined BAU
## 152 PFAS PFAS TWI BAU Female Undefined BAU
## 153 Vitamin D Vitamin D recommendation BAU Male Undefined BAU
## 154 PFAS PFAS TWI BAU Male Undefined BAU
## mean
## 1 -0.15625100
## 2 0.01813625
## 3 -0.06412974
## 4 -0.19763757
## 5 -0.15262578
## 6 -0.15322440
## 7 -0.23437649
## 8 0.02720438
## 9 -0.09463548
## 10 -0.28128638
## 11 -0.21996719
## 12 -0.15843589
## 13 -0.06412974
## 14 -0.19763757
## 15 -0.15262578
## 16 -0.15322440
## 17 -0.09463548
## 18 -0.28128638
## 19 -0.21996719
## 20 -0.15843589
## 21 -0.06412974
## 22 -0.19763757
## 23 -0.15262578
## 24 -0.15322440
## 25 -0.09463548
## 26 -0.28128638
## 27 -0.21996719
## 28 -0.15843589
## 29 -0.06412974
## 30 -0.19763757
## 31 -0.15262578
## 32 -0.15322440
## 33 -0.09463548
## 34 -0.28128638
## 35 -0.21996719
## 36 -0.15843589
## 37 -0.06412974
## 38 -0.19763757
## 39 -0.15262578
## 40 -0.15322440
## 41 -0.09463548
## 42 -0.28128638
## 43 -0.21996719
## 44 -0.15843589
## 45 -0.06412974
## 46 -0.19763757
## 47 -0.15262578
## 48 -0.15322440
## 49 -0.09463548
## 50 -0.28128638
## 51 -0.21996719
## 52 -0.15843589
## 53 -0.06412974
## 54 -0.19763757
## 55 -0.15262578
## 56 -0.15322440
## 57 -0.09463548
## 58 -0.28128638
## 59 -0.21996719
## 60 -0.15843589
## 61 -0.06412974
## 62 -0.19763757
## 63 -0.15262578
## 64 -0.15322440
## 65 -0.09463548
## 66 -0.28128638
## 67 -0.21996719
## 68 -0.15843589
## 69 -0.06412974
## 70 -0.19763757
## 71 -0.15262578
## 72 -0.15322440
## 73 -0.09463548
## 74 -0.28128638
## 75 -0.21996719
## 76 -0.15843589
## 77 0.02208490
## 78 -0.06412974
## 79 -0.19763757
## 80 -0.15262578
## 81 -0.15322440
## 82 0.03312735
## 83 -0.09463548
## 84 -0.28128638
## 85 -0.21996719
## 86 -0.15843589
## 87 -0.06412974
## 88 -0.19763757
## 89 -0.15262578
## 90 -0.15322440
## 91 -0.09463548
## 92 -0.28128638
## 93 -0.21996719
## 94 -0.15843589
## 95 -0.06412974
## 96 -0.19763757
## 97 -0.15262578
## 98 -0.15322440
## 99 -0.09463548
## 100 -0.28128638
## 101 -0.21996719
## 102 -0.15843589
## 103 -0.06412974
## 104 -0.19763757
## 105 -0.15262578
## 106 -0.15322440
## 107 -0.09463548
## 108 -0.28128638
## 109 -0.21996719
## 110 -0.15843589
## 111 -0.06412974
## 112 -0.19763757
## 113 -0.15262578
## 114 -0.15322440
## 115 -0.09463548
## 116 -0.28128638
## 117 -0.21996719
## 118 -0.15843589
## 119 -0.06412974
## 120 -0.19763757
## 121 -0.15262578
## 122 -0.15322440
## 123 -0.09463548
## 124 -0.28128638
## 125 -0.21996719
## 126 -0.15843589
## 127 -0.06412974
## 128 -0.19763757
## 129 -0.15262578
## 130 -0.15322440
## 131 -0.09463548
## 132 -0.28128638
## 133 -0.21996719
## 134 -0.15843589
## 135 -0.06412974
## 136 -0.19763757
## 137 -0.15262578
## 138 -0.15322440
## 139 -0.09463548
## 140 -0.28128638
## 141 -0.21996719
## 142 -0.15843589
## 143 -0.06412974
## 144 -0.19763757
## 145 -0.15262578
## 146 -0.15322440
## 147 -0.09463548
## 148 -0.28128638
## 149 -0.21996719
## 150 -0.15843589
## 151 1.00000000
## 152 0.00000000
## 153 1.00000000
## 154 1.00000000
oprint(summary(population,"mean"))
## Gender Age mean
## 1 Female 0 - 4 125040
## 2 Male 0 - 4 130884
## 3 Female 10 - 14 151113
## 4 Male 10 - 14 157712
## 5 Female 15 - 19 144441
## 6 Male 15 - 19 152230
## 7 Female 20 - 24 152265
## 8 Male 20 - 24 161679
## 9 Female 25 - 29 172593
## 10 Male 25 - 29 183092
## 11 Female 30 - 34 169653
## 12 Male 30 - 34 181115
## 13 Female 35 - 39 174660
## 14 Male 35 - 39 186122
## 15 Female 40 - 44 168547
## 16 Male 40 - 44 177928
## 17 Female 45 - 49 154391
## 18 Male 45 - 49 159982
## 19 Female 5 - 9 149633
## 20 Male 5 - 9 156654
## 21 Female 50 - 54 176612
## 22 Male 50 - 54 179182
## 23 Female 55 - 59 185152
## 24 Male 55 - 59 183719
## 25 Female 60 - 64 183336
## 26 Male 60 - 64 176283
## 27 Female 65 - 69 185685
## 28 Male 65 - 69 171275
## 29 Female 70 - 74 186034
## 30 Male 70 - 74 163697
## 31 Female 75 - 79 118190
## 32 Male 75 - 79 93987
## 33 Female 80 - 84 96256
## 34 Male 80 - 84 65140
## 35 Female 85+ 103429
## 36 Male 85+ 47581
## 37 Female Undefined 2797030
## 38 Male Undefined 2728262
oprint(summary(RR,"mean"))
## Exposure_agent Response ER_function Scaling Scenario Gender
## 1 Fish All-cause mortality RR None BAU Female
## 2 Omega3 Breast cancer RR None BAU Female
## 3 Fish Depression RR None BAU Female
## 4 Omega3 CHD2 mortality Relative Hill None BAU Female
## 5 Fish All-cause mortality RR None BAU Male
## 6 Omega3 Breast cancer RR None BAU Male
## 7 Fish Depression RR None BAU Male
## 8 Omega3 CHD2 mortality Relative Hill None BAU Male
## 9 Fish All-cause mortality RR None BAU Female
## 10 Omega3 Breast cancer RR None BAU Female
## 11 Fish Depression RR None BAU Female
## 12 Omega3 CHD2 mortality Relative Hill None BAU Female
## 13 Fish All-cause mortality RR None BAU Male
## 14 Omega3 Breast cancer RR None BAU Male
## 15 Fish Depression RR None BAU Male
## 16 Omega3 CHD2 mortality Relative Hill None BAU Male
## 17 Fish All-cause mortality RR None BAU Female
## 18 Omega3 Breast cancer RR None BAU Female
## 19 Fish Depression RR None BAU Female
## 20 Omega3 CHD2 mortality Relative Hill None BAU Female
## 21 Fish All-cause mortality RR None BAU Male
## 22 Omega3 Breast cancer RR None BAU Male
## 23 Fish Depression RR None BAU Male
## 24 Omega3 CHD2 mortality Relative Hill None BAU Male
## 25 Fish All-cause mortality RR None BAU Female
## 26 Omega3 Breast cancer RR None BAU Female
## 27 Fish Depression RR None BAU Female
## 28 Omega3 CHD2 mortality Relative Hill None BAU Female
## 29 Fish All-cause mortality RR None BAU Male
## 30 Omega3 Breast cancer RR None BAU Male
## 31 Fish Depression RR None BAU Male
## 32 Omega3 CHD2 mortality Relative Hill None BAU Male
## 33 Fish All-cause mortality RR None BAU Female
## 34 Omega3 Breast cancer RR None BAU Female
## 35 Fish Depression RR None BAU Female
## 36 Omega3 CHD2 mortality Relative Hill None BAU Female
## 37 Fish All-cause mortality RR None BAU Male
## 38 Omega3 Breast cancer RR None BAU Male
## 39 Fish Depression RR None BAU Male
## 40 Omega3 CHD2 mortality Relative Hill None BAU Male
## 41 Fish All-cause mortality RR None BAU Female
## 42 Omega3 Breast cancer RR None BAU Female
## 43 Fish Depression RR None BAU Female
## 44 Omega3 CHD2 mortality Relative Hill None BAU Female
## 45 Fish All-cause mortality RR None BAU Male
## 46 Omega3 Breast cancer RR None BAU Male
## 47 Fish Depression RR None BAU Male
## 48 Omega3 CHD2 mortality Relative Hill None BAU Male
## 49 Fish All-cause mortality RR None BAU Female
## 50 Omega3 Breast cancer RR None BAU Female
## 51 Fish Depression RR None BAU Female
## 52 Omega3 CHD2 mortality Relative Hill None BAU Female
## 53 Fish All-cause mortality RR None BAU Male
## 54 Omega3 Breast cancer RR None BAU Male
## 55 Fish Depression RR None BAU Male
## 56 Omega3 CHD2 mortality Relative Hill None BAU Male
## 57 Fish All-cause mortality RR None BAU Female
## 58 Omega3 Breast cancer RR None BAU Female
## 59 Fish Depression RR None BAU Female
## 60 Omega3 CHD2 mortality Relative Hill None BAU Female
## 61 Fish All-cause mortality RR None BAU Male
## 62 Omega3 Breast cancer RR None BAU Male
## 63 Fish Depression RR None BAU Male
## 64 Omega3 CHD2 mortality Relative Hill None BAU Male
## 65 Fish All-cause mortality RR None BAU Female
## 66 Omega3 Breast cancer RR None BAU Female
## 67 Fish Depression RR None BAU Female
## 68 Omega3 CHD2 mortality Relative Hill None BAU Female
## 69 Fish All-cause mortality RR None BAU Male
## 70 Omega3 Breast cancer RR None BAU Male
## 71 Fish Depression RR None BAU Male
## 72 Omega3 CHD2 mortality Relative Hill None BAU Male
## 73 Fish All-cause mortality RR None BAU Female
## 74 Omega3 Breast cancer RR None BAU Female
## 75 Fish Depression RR None BAU Female
## 76 Omega3 CHD2 mortality Relative Hill None BAU Female
## 77 Fish All-cause mortality RR None BAU Male
## 78 Omega3 Breast cancer RR None BAU Male
## 79 Fish Depression RR None BAU Male
## 80 Omega3 CHD2 mortality Relative Hill None BAU Male
## 81 Fish All-cause mortality RR None BAU Female
## 82 Omega3 Breast cancer RR None BAU Female
## 83 Fish Depression RR None BAU Female
## 84 Omega3 CHD2 mortality Relative Hill None BAU Female
## 85 Fish All-cause mortality RR None BAU Male
## 86 Omega3 Breast cancer RR None BAU Male
## 87 Fish Depression RR None BAU Male
## 88 Omega3 CHD2 mortality Relative Hill None BAU Male
## 89 Fish All-cause mortality RR None BAU Female
## 90 Omega3 Breast cancer RR None BAU Female
## 91 Fish Depression RR None BAU Female
## 92 Omega3 CHD2 mortality Relative Hill None BAU Female
## 93 Fish All-cause mortality RR None BAU Male
## 94 Omega3 Breast cancer RR None BAU Male
## 95 Fish Depression RR None BAU Male
## 96 Omega3 CHD2 mortality Relative Hill None BAU Male
## 97 Fish All-cause mortality RR None BAU Female
## 98 Omega3 Breast cancer RR None BAU Female
## 99 Fish Depression RR None BAU Female
## 100 Omega3 CHD2 mortality Relative Hill None BAU Female
## 101 Fish All-cause mortality RR None BAU Male
## 102 Omega3 Breast cancer RR None BAU Male
## 103 Fish Depression RR None BAU Male
## 104 Omega3 CHD2 mortality Relative Hill None BAU Male
## 105 Fish All-cause mortality RR None BAU Female
## 106 Omega3 Breast cancer RR None BAU Female
## 107 Fish Depression RR None BAU Female
## 108 Omega3 CHD2 mortality Relative Hill None BAU Female
## 109 Fish All-cause mortality RR None BAU Male
## 110 Omega3 Breast cancer RR None BAU Male
## 111 Fish Depression RR None BAU Male
## 112 Omega3 CHD2 mortality Relative Hill None BAU Male
## 113 Fish All-cause mortality RR None BAU Female
## 114 Omega3 Breast cancer RR None BAU Female
## 115 Fish Depression RR None BAU Female
## 116 Omega3 CHD2 mortality Relative Hill None BAU Female
## 117 Fish All-cause mortality RR None BAU Male
## 118 Omega3 Breast cancer RR None BAU Male
## 119 Fish Depression RR None BAU Male
## 120 Omega3 CHD2 mortality Relative Hill None BAU Male
## 121 Fish All-cause mortality RR None BAU Female
## 122 Omega3 Breast cancer RR None BAU Female
## 123 Fish Depression RR None BAU Female
## 124 Omega3 CHD2 mortality Relative Hill None BAU Female
## 125 Fish All-cause mortality RR None BAU Male
## 126 Omega3 Breast cancer RR None BAU Male
## 127 Fish Depression RR None BAU Male
## 128 Omega3 CHD2 mortality Relative Hill None BAU Male
## 129 Fish All-cause mortality RR None BAU Female
## 130 Omega3 Breast cancer RR None BAU Female
## 131 Fish Depression RR None BAU Female
## 132 Omega3 CHD2 mortality Relative Hill None BAU Female
## 133 Fish All-cause mortality RR None BAU Male
## 134 Omega3 Breast cancer RR None BAU Male
## 135 Fish Depression RR None BAU Male
## 136 Omega3 CHD2 mortality Relative Hill None BAU Male
## 137 Fish All-cause mortality RR None BAU Female
## 138 Omega3 Breast cancer RR None BAU Female
## 139 Fish Depression RR None BAU Female
## 140 Omega3 CHD2 mortality Relative Hill None BAU Female
## 141 Fish All-cause mortality RR None BAU Male
## 142 Omega3 Breast cancer RR None BAU Male
## 143 Fish Depression RR None BAU Male
## 144 Omega3 CHD2 mortality Relative Hill None BAU Male
## Age mean
## 1 0 - 4 0.9358703
## 2 0 - 4 0.8023624
## 3 0 - 4 0.8473742
## 4 0 - 4 0.8467756
## 5 0 - 4 0.9053645
## 6 0 - 4 0.7187136
## 7 0 - 4 0.7800328
## 8 0 - 4 0.8415641
## 9 10 - 14 0.9358703
## 10 10 - 14 0.8023624
## 11 10 - 14 0.8473742
## 12 10 - 14 0.8467756
## 13 10 - 14 0.9053645
## 14 10 - 14 0.7187136
## 15 10 - 14 0.7800328
## 16 10 - 14 0.8415641
## 17 15 - 19 0.9358703
## 18 15 - 19 0.8023624
## 19 15 - 19 0.8473742
## 20 15 - 19 0.8467756
## 21 15 - 19 0.9053645
## 22 15 - 19 0.7187136
## 23 15 - 19 0.7800328
## 24 15 - 19 0.8415641
## 25 20 - 24 0.9358703
## 26 20 - 24 0.8023624
## 27 20 - 24 0.8473742
## 28 20 - 24 0.8467756
## 29 20 - 24 0.9053645
## 30 20 - 24 0.7187136
## 31 20 - 24 0.7800328
## 32 20 - 24 0.8415641
## 33 25 - 29 0.9358703
## 34 25 - 29 0.8023624
## 35 25 - 29 0.8473742
## 36 25 - 29 0.8467756
## 37 25 - 29 0.9053645
## 38 25 - 29 0.7187136
## 39 25 - 29 0.7800328
## 40 25 - 29 0.8415641
## 41 30 - 34 0.9358703
## 42 30 - 34 0.8023624
## 43 30 - 34 0.8473742
## 44 30 - 34 0.8467756
## 45 30 - 34 0.9053645
## 46 30 - 34 0.7187136
## 47 30 - 34 0.7800328
## 48 30 - 34 0.8415641
## 49 35 - 39 0.9358703
## 50 35 - 39 0.8023624
## 51 35 - 39 0.8473742
## 52 35 - 39 0.8467756
## 53 35 - 39 0.9053645
## 54 35 - 39 0.7187136
## 55 35 - 39 0.7800328
## 56 35 - 39 0.8415641
## 57 40 - 44 0.9358703
## 58 40 - 44 0.8023624
## 59 40 - 44 0.8473742
## 60 40 - 44 0.8467756
## 61 40 - 44 0.9053645
## 62 40 - 44 0.7187136
## 63 40 - 44 0.7800328
## 64 40 - 44 0.8415641
## 65 45 - 49 0.9358703
## 66 45 - 49 0.8023624
## 67 45 - 49 0.8473742
## 68 45 - 49 0.8467756
## 69 45 - 49 0.9053645
## 70 45 - 49 0.7187136
## 71 45 - 49 0.7800328
## 72 45 - 49 0.8415641
## 73 5 - 9 0.9358703
## 74 5 - 9 0.8023624
## 75 5 - 9 0.8473742
## 76 5 - 9 0.8467756
## 77 5 - 9 0.9053645
## 78 5 - 9 0.7187136
## 79 5 - 9 0.7800328
## 80 5 - 9 0.8415641
## 81 50 - 54 0.9358703
## 82 50 - 54 0.8023624
## 83 50 - 54 0.8473742
## 84 50 - 54 0.8467756
## 85 50 - 54 0.9053645
## 86 50 - 54 0.7187136
## 87 50 - 54 0.7800328
## 88 50 - 54 0.8415641
## 89 55 - 59 0.9358703
## 90 55 - 59 0.8023624
## 91 55 - 59 0.8473742
## 92 55 - 59 0.8467756
## 93 55 - 59 0.9053645
## 94 55 - 59 0.7187136
## 95 55 - 59 0.7800328
## 96 55 - 59 0.8415641
## 97 60 - 64 0.9358703
## 98 60 - 64 0.8023624
## 99 60 - 64 0.8473742
## 100 60 - 64 0.8467756
## 101 60 - 64 0.9053645
## 102 60 - 64 0.7187136
## 103 60 - 64 0.7800328
## 104 60 - 64 0.8415641
## 105 65 - 69 0.9358703
## 106 65 - 69 0.8023624
## 107 65 - 69 0.8473742
## 108 65 - 69 0.8467756
## 109 65 - 69 0.9053645
## 110 65 - 69 0.7187136
## 111 65 - 69 0.7800328
## 112 65 - 69 0.8415641
## 113 70 - 74 0.9358703
## 114 70 - 74 0.8023624
## 115 70 - 74 0.8473742
## 116 70 - 74 0.8467756
## 117 70 - 74 0.9053645
## 118 70 - 74 0.7187136
## 119 70 - 74 0.7800328
## 120 70 - 74 0.8415641
## 121 75 - 79 0.9358703
## 122 75 - 79 0.8023624
## 123 75 - 79 0.8473742
## 124 75 - 79 0.8467756
## 125 75 - 79 0.9053645
## 126 75 - 79 0.7187136
## 127 75 - 79 0.7800328
## 128 75 - 79 0.8415641
## 129 80 - 84 0.9358703
## 130 80 - 84 0.8023624
## 131 80 - 84 0.8473742
## 132 80 - 84 0.8467756
## 133 80 - 84 0.9053645
## 134 80 - 84 0.7187136
## 135 80 - 84 0.7800328
## 136 80 - 84 0.8415641
## 137 85+ 0.9358703
## 138 85+ 0.8023624
## 139 85+ 0.8473742
## 140 85+ 0.8467756
## 141 85+ 0.9053645
## 142 85+ 0.7187136
## 143 85+ 0.7800328
## 144 85+ 0.8415641
###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)
ggplot(amount@output, aes(x=Gender, weight=amountResult, fill=Kala))+geom_bar()+
labs(
title="Kalansyönti Suomessa ikäryhmittäin",
y="Syönti (g/d)"
)
ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")
## Saving 7 x 5 in image
plot_ly(trim(total_amount), x=~Kala, y=~total_amountResult, color=~Kala, type="bar") %>%
layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
ggplot(conc_pfas@output, aes(x=Fish, y=conc_pfasResult))+geom_point() # Inputed data for missing species.
ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
scale_x_log10()+
stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
scale_linetype_manual(values=c("dotted","solid","twodash"))+
labs(
title="PFAS concentration in fishes in Finland",
x="PFAS concentration (ng/g fresh weight)",
y="Cumulative probability"
)
# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")
## Saving 7 x 5 in image
ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
facet_wrap(~Compound, scales="free_x")
#ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
# aes(x=Gender, weight=expo_dirResult,fill=Fish))+geom_bar()+
# facet_wrap(~Compound, scales="free_y")+
# labs(title="Eri yhdisteiden saanti kalasta")
#ggsave("Yhdisteiden saanti kalasta Suomessa.svg")
tmp <- exposure #/ Ovariable(
# output = data.frame(
# Exposure_agent = c("Fish","Vitamin D", "Omega3", "ALA", "DHA", "TEQ", "PFAS"),
# Result = c(1, 1, 1000, 1000, 1000, 1, 1)
# ),
# marginal = c(TRUE, FALSE)
#)
plot_ly(trim(tmp), x=~exposureSource, y=~exposureResult, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))
ggplot(exposure@output, aes(x=Gender, weight=exposureResult, fill=Gender))+geom_bar()+
facet_wrap(~Exposure_agent, scales="free_y")+
labs(
title="Exposure to compounds",
y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
)
cat("Kalaperäisiä tautitaakkoja Suomessa\n")
## Kalaperäisiä tautitaakkoja Suomessa
if(openv$N>1) {
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Vaikutus = tmp$Response,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
tmp <- data.frame(
Terveysvaikutus = tmp$Response,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
}
ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()
ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()
plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]
## Loading objects:
## makeGraph
gr <- makeGraph(gr)
## Loading required package: DiagrammeR
## Loading objects:
## formatted
## Loading objects:
## chooseGr
#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")
## Loading objects:
## showind
## binoptest
## showLoctable
## ovashapetest
showLoctable()
showind()
## subgrouping is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.